Quantitative integration of spatial and temporal information provided by time-lapse (4D) seismic surveys to dynamic reservoir models calls for an efficient and effective workflow. To solve this issue, we propose a novel workflow which uses a Bayesian/MCMC approach and experimental design-based proxies for selected 4D seismic observables to update dynamic reservoir models. This methodology includes the following steps: (1) create probability maps to select locations where 4D seismic data is assimilated; (2) run a sensitivity analysis; (3) create high-order proxy models; and (4) run an MCMC inversion to determine a set of models that best fit the 4D seismic data and quantify uncertainty. This new workflow has been applied in 3 cases including two synthetic models and one field case. This first synthetic example is called the Imperial College Fault Model (ICFM).The second synthetic model is a fluvial reservoir model with 10 uncertain parameters. The field example is a
deepwater turbidite reservoir undergoing a waterflood with a reasonably long production history and high-quality 4D seismic data. Following the four steps of this workflow, all the models are successfully history matched by conditioning to 4D seismic data. Uncertainty quantification was also provided as part of the MCMC inversion. We also compare different scenarios using production data and/or 4D seismic data in the model updating process to show the value of the 4D seismic data. For our field case, the updated models can be used for production forecasting, reserves booking and identification of further development opportunities.
Jin, Long (Shell) | Gao, Guohua (Shell) | Vink, Jeroen C. (Shell Intl. E&P Co.) | Chen, Chaohui (Shell International EP) | Weber, Daniel (Shell Intl. E&P Co.) | Alpak, Faruk Omer (Shell Intl. E&P Co.) | van den Hoek, Paul (Shell) | Pirmez, Carlos (Shell Intl. E&P Co.)
Quantitative integration of 4D seismic data with production data into reservoir models is a challenging task. One important issue is how to properly quantify the uncertainty, or the posterior probability distribution (PPD). The Very Fast Simulated Annealing (VFSA) is a stochastic searching method, whereas the Simultaneous Perturbation and Multivariate Interpolation (SPMI) is a model-based local searching method. The stochastic features of the VFSA provide the feasibility of identifying possible multiple peaks of a PPD, but it converges very slowly. On the other hand, the model-based SPMI method has the advantages of effectively utilizing the smooth features of an objective function, and thus can converge to local optimum very quickly. More importantly, the Hessian of the objective function, or the covariance matrix of the PPD, can be estimated by the SPMI method with satisfactory accuracy. However, it is very difficult to identify multiple optima by applying the SPMI method alone. In this paper, we propose an efficient joint inversion workflow by appropriately integrating the two derivative
free optimization (DFO) methods. The complementary features of the two methods can further improve both applicability and efficiency of this joint inversion workflow. We tested the workflow with a 3D synthetic model and a real field case. Our results show that the integrated method is efficient and can deliver good results for jointly assimilating 4D seismic and production data.
Van Essen, Gijs (Shell International E & P) | Jimenez, Eduardo (Shell) | Przybysz-jarnut, Justyna Katarzyna (IBM T J Watson Research Center) | Horesh, Lior (Shell Intl. E&P Co.) | Douma, Sippe G. (Shell) | van den Hoek, Paul (IBM T.J. Watson R&D Center) | Conn, Andrew (IBM) | Mello, Ulisses T.
Time-lapse (4D) seismic attributes can provide valuable information on the fluid flow within subsurface reservoirs. This spatially-rich source of information complements the poor areal information obtainable from production well data. While fusion of information from the two sources holds great promise, in practice, this task is far from trivial. Joint Inversion is complex for many reasons, including different time and spatial scales, the fact that the coupling mechanisms between the various parameters are often not well established, the localized nature of the required model updates, and the necessity to integrate multiple data. These concerns limit the applicability of many data-assimilation techniques. Adjoint-based methods are free of these drawbacks but their implementation generally requires extensive programming effort. In this study we present a workflow that exploits the adjoint functionality that modern simulators offer for production data to consistently assimilate inverted 4D seismic attributes without the need for re-programming of the adjoint code. Here we discuss a novel workflow which we applied to assimilate production data and 4D seismic data from a synthetic reservoir model, which acts as the real - yet unknown - reservoir. Synthetic production data and 4D seismic data were created from this model to study the performance of the adjoint-based method. The seamless structure of the workflow allowed rapid setup of the data assimilation process, while execution of the process was reduced significantly. The resulting reservoir model updates displayed a considerable improvement in matching the saturation distribution in the field. This work was carried out as part of a joint Shell-IBM research project.
van den Hoek, Paul (Shell) | Mahani, Hassan (Shell Intl. E&P Co.) | Sorop, Tibi (Shell) | Brooks, David (AAR Energy) | Zwaan, Marcel (Shell Intl. E&P Co.) | Sen, Subrata (Shell India Markets Private Ltd) | Shuaili, Khalfan (PDO) | Saadi, Faisal (PDO)
Polymers exhibit non-Newtonian rheological behavior, such as in-situ shear-thinning and shear-thickening effects. This has a significant impact on pressure decline signature as exhibited during Pressure Fall-Off (PFO) tests. Therefore, applying a different PFO interpretation method, compared to conventional approaches for Newtonian fluids is required.
This paper presents a simple and practical methodology to infer the in-situ polymer rheology from PFO tests performed during polymer injection. This is based on a combination of numerical flow simulations and analytical pressure transient calculations, resulting in generic type curves that are used to compute consistency index and flow behavior index, in addition to the usual reservoir parameters (kh, faulting, etc.) and parameters relating to (possible) induced fracturing during injection (fracture length and height). The tools and workflows are illustrated by a number of field examples of polymer PFO, which will also demonstrate how the polymer bank can be located from the data.
Kaleta, Malgorzata (Shell Global Solutions International BV) | Van Essen, Gijs (Shell International E & P) | Van Doren, Jorn (Shell) | Bennett, Richard (Shell International Exploration and Production Co.) | van Beest, B.W.H. (Shell) | van den Hoek, Paul (Shell) | Brint, John Forsyth (Shell Exploration & Production) | Woodhead, Timothy Jonathan (Shell International Ltd.)
In the petroleum industry, history-matched reservoir models are used to aid the field development decision-making process. Traditionally, models have been history-matched by reservoir engineers in the dynamic domain only. Ideally, if any changes are required to static parameters as result of history matching the dynamic model, then these should be reflected directly in the static reservoir model, ensuring consistency between the static and dynamic domain. In addition, static model uncertainties are often not evaluated in the dynamic domain, which can result in the detailed modeling of geological features that have little impact on the dynamic behavior of the reservoir or the resulting development decision.
This paper demonstrates a workflow where the reservoir simulator and static modeling package are closely linked to promote a more integrated approach to reservoir model construction, facilitating the interaction between subsurface disciplines. Using either the reservoir simulator or the static modeling package as the platform, the output of the workflow is a sensitivity analysis of the uncertainties related to structure, rock properties, fluids and rock-fluid interactions. Computer-assisted history matching methods (i.e. adjoint-based and Design of Experiments) are used to find the parameter values that result in a history match model. The workflow is described for both a synthetic model and also a reservoir model from a real field case.
This methodology results in improved history-matched models and a better understanding of the static and dynamic subsurface uncertainties and their importance, leading to more informed decision-making. Furthermore, it is anticipated that it will result in faster accomplishment of the history matching studies.
The method presented here can significantly enhance the understanding of the impact of both static and dynamic subsurface uncertainties on development decisions. In addition, it offers a platform where all subsurface professionals involved in reservoir model construction and simulation can more optimally focus their efforts on improving the integrated understanding of their reservoirs.
Zwaan, Marcel (Shell Intl. E&P Co.) | Hartmans, Rob (Petroleum Development Oman) | Schoofs, Stan (Shell Intl. E&P Co.) | De Zwart, Albert Hendrik (Shell Intl EP Co) | Rocco, Guillermo (Petroleum Development Oman) | Adawi, Rashid (PDO) | Saadi, Faisal (PDO) | Shuaili, Khalfan (PDO) | Lopez, Jorge L. (Shell Intl. E&P Co.) | Ita, joel (Shell Global Solutions International) | Lhomme, Tanguy Plerre Yves (Delft U. of Technology) | Sorop, Tibi (Shell) | Qiu, Yuan (Shell International B.V.) | van den Hoek, Paul (Shell) | Al Kindy, Fahad (Petroleum Development Oman) | Al-Busaidi, Said (Petroleum Development Oman) | Fraser, John Elliot (Petroleum Development Oman)
PDO has implemented Enhanced Oil Recovery (EOR) methods including thermal, chemical and miscible gas injection projects in several fields. In the initial phase of these EOR projects, well and reservoir surveillance is key to increase the understanding of the effectiveness of the EOR processes in the various reservoirs.
However, the interpretation of this advanced surveillance data and integration into well and reservoir management workflows is still challenging. This paper describes the results of the integrated workflows for the interpretation, modeling and integration of surveillance data in four EOR projects. The surveillance methods include geomechanical modeling, thermal reservoir modeling and monitoring through time lapse seismic, surface deformation, microseismic, temperature, pressure and saturation logging. This paper shows that the surveillance has contributed to the understanding of recovery mechanism in a pattern steam flood, it has supported decisions well-recompletions and work-overs and it has supported decisions on injection rates and surveillance planning in the chemical flood project.